Examining the experiences and needs of caregivers of older adults with mental health problems
Bibliographic record
Abstract
Little is known about caregivers who support older adults with mental health problems (older adult mental health caregivers), with the extant research focusing on caregivers of older adults with cognitive difficulties. Older adult mental health caregivers have the unique experiences of care provision and service navigation for their recipient’s physical and mental health needs, which may elevate caregiver burden and mental health problems. Conflict often exists between the caregiver role and other personal roles, negatively impacting mental health and service use. However, barriers to this population’s mental health service use, and their ability to meet both their own and their care recipient’s needs are unknown. This research aimed to examine the experiences of older adult mental health caregivers, particularly how they balance meeting their own mental health needs and those of their care recipient. Caregivers identifying as supporting an older adult (60+) experiencing a mental health problem were recruited from public mental health services, community organizations, and social media in Manitoba (N=11). Participants completed an online survey and remote semi-structured interview examining their caregiving experiences. An interpretivist frame and Framework approach were utilized, resulting in five emerging themes depicting caregiving experiences, impact on caregiver mental health, barriers to supports and unmet support needs, balancing conflicting roles, and facilitators and barriers to navigating mental health services for their care recipient. Central to participants’ experiences were their care task focus, the cyclical nature of caregiving, care recipients’ reluctance toward external care, acknowledging mental health symptoms, and help-seeking. For those who engaged in mental health service navigation for their care recipient, it was unclear if this interfered with their ability to seek services for themselves. Conflict existed between various roles which was described as negatively impacting self-care and emotional well-being. Recommendations for service providers include flexibility in frequency, duration, timing, and scope of services to adjust to caregivers’ emotional and social support needs and the fluctuation in care recipients’ mental health over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".